Galileo vs MLflow
Both are observability & tracing tools. Here is how they actually differ on price, billing model and deployment.
Galileo
The best-funded evaluation-and-observability platform in this space, built around proprietary Luna eval models. Not to be confused with the Google-acquired text-to-UI tool of the same name. Real pricing is sales-led above $100/mo.
MLflow
The open-source ML platform that grew a serious GenAI half. MLflow 3 adds OpenTelemetry-compatible tracing, LLM judges and review apps - free and self-hostable, with Databricks selling the managed version. The best zero-cost option if you can run infrastructure.
| Galileo | MLflow | |
|---|---|---|
| Category | Observability & Tracing | Observability & Tracing |
| Our rating | 4/5 | 4/5 |
| Starting price | $100/mo | $0 (open source) |
| Billing meter | span | No usage metering |
| Free plan | Yes | Yes |
| Free self-hosting | No or paid tier only | Yes, free |
| Best for | Enterprise GenAI teams that have been burned by production hallucinations and want research-grade eval metrics plus guardrails, and can run a sales process | Teams that already run MLflow for classical ML, Databricks customers, and anyone who wants a genuinely free and complete self-hosted platform and has the operational capacity to run it. |
Our verdict on Galileo
Galileo is the best-funded and arguably most research-forward platform here, and the Luna eval models are a real bet on making evaluation cheap enough to run continuously. Two caveats. First, make sure you're evaluating this Galileo and not the design tool that shares the name - the pricing and reviews get conflated constantly. Second, above the $100 Pro tier everything is contact-sales, and self-host is Enterprise-only, so this is a platform you buy through a rep, not a card.
Full Galileo review →Our verdict on MLflow
MLflow is the strongest zero-cost option in the category, with the caveat that free software is not free to operate. MLflow 3 turned what was an experiment-tracking tool into a real GenAI platform - OpenTelemetry-compatible tracing from a single line of code, built-in and custom LLM judges, review apps that collect expert feedback and align automated judges against it, and evaluation datasets built directly from production traces. It is Apache 2.0 and the open-source build is complete rather than a gated teaser, which is more than can be said for several commercial competitors advertising self-hosting. Two honest caveats. The UI is functional rather than pleasant, and it shows its lineage as a tool built for ML engineers rather than application developers. And the genuinely best-governed experience - Unity Catalog trace storage in OTel Delta tables, no storage cap, SQL queryable - is available on Databricks, which is where the commercial gravity sits. If you already run MLflow or Databricks, this is close to automatic.
Full MLflow review →Frequently Asked Questions
What is the main difference between Galileo and MLflow?
Galileo: Enterprise GenAI teams that have been burned by production hallucinations and want research-grade eval metrics plus guardrails, and can run a sales process MLflow: Teams that already run MLflow for classical ML, Databricks customers, and anyone who wants a genuinely free and complete self-hosted platform and has the operational capacity to run it. Both sit in Observability & Tracing, so the decision usually comes down to billing model and deployment rather than raw capability.
Which is cheaper, Galileo or MLflow?
It depends entirely on your workload shape, because they meter differently - Galileo bills on span and MLflow bills on no usage metering. Published starting prices are $100/mo and $0 (open source) respectively, but those numbers are not comparable until you apply them to the same traffic. Use our cost calculator to model both against your own request volume and span count.
Can I self-host Galileo or MLflow?
Galileo: No or paid tier only. MLflow: Yes, free. Free self-hosting means no licence fee, not no cost - you still own the infrastructure, upgrades and on-call.